Comparison of in vivo derived and scaled in vitro metabolic rate constants for several volatile organic compounds (VOCs)

Drug-drug interaction prediction of ziritaxestat using a physiologically based enzyme and transporter pharmacokinetic network interaction model

Ziritaxestat, an autotaxin inhibitor, was under development for the treatment of idiopathic pulmonary fibrosis. It is a substrate of cytochrome P450 3A4 (CYP3A4) and P-glycoprotein and a weak inhibitor of the CYP3A4 and OATP1B1 pathways. We developed a physiologically based pharmacokinetic (PBPK) network interaction model for ziritaxestat that incorporated its metabolic and transporter pathways, enabling prediction of its potential as a victim or perpetrator of drug-drug interactions (DDIs). Concurrently, we evaluated CYP3A4 autoinhibition, including time-dependent inhibition. In vitro information and clinical data from healthy volunteer studies were used for model building and validation. DDIs with rifampin, itraconazole, voriconazole, pravastatin, and rosuvastatin were predicted, followed by validation against a test dataset. DDIs of ziritaxestat as a victim or perpetrator were simulated using the final model. Predicted-to-observed DDI ratios for the maximum plasma concentration (Cmax ) and the area under the plasma concentration-time curve (AUC) were within a two-fold ratio for both the metabolic and transporter-mediated simulated DDIs. The predicted impact of autoinhibition/autoinduction or time-dependent inhibition of CYP3A4 was a 12% decrease in exposure. Model-based predictions for ziritaxestat as a victim of DDIs with a moderate CYP3A4 inhibitor (fluconazole) suggested a 2.6-fold increase in the AUC of ziritaxestat, while multiple doses of a strong inhibitor (voriconazole) would increase the AUC by 15-fold. Efavirenz would yield a three-fold decrease in the AUC of ziritaxestat. As a perpetrator, ziritaxestat was predicted to increase the AUC of the CYP3A4 index substrate midazolam by 2.7-fold. An overarching PBPK model was developed that could predict DDI liability of ziritaxestat for both CYP3A4 and the transporter pathways.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.